How to Transition from Another ELN: A Practical Migration Strategy for Research Labs
Here's how to move from a legacy ELN, paper notebooks, spreadsheets, or mixed ELN/LIMS setup with a migration plan that protects historical data, keeps samples traceable, and helps you adopt the new system faster.
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TL;DR
- Why migrations fail.
ELN transitions break when teams move records before defining the workflows, data relationships, ownership, and validation behind them. Common traps include importing sample names, IDs, and storage locations before cleaning them, recreating old workflow friction in a cleaner interface, and treating compliance as a final check instead of a design requirement for signatures, permissions, and retention. - Audit before you move.
Before leaving your current ELN, inventory every record, sample, protocol, file, user, permission, integration, and paper archive that must survive, plus where people actually work outside the system (Excel trackers, freezer notebooks, OneNote, PDFs, email approvals, local instrument computers). Multi-site labs should map which site owns which notebooks and naming rules. - Migrate, archive, or rebuild.
Decide record by record whether content moves as structured data, stays as a preserved read-only archive, or gets rebuilt as a cleaner workflow template. Prioritize the protocols, active samples, freezer locations, and open studies scientists need in the first 30 to 90 days, and keep older records retrievable for audits, manuscripts, grant reports, and IP review. - Prove data integrity.
Protect data integrity by showing migrated records stay complete, accurate, attributable, legible, traceable, and retrievable. Standards like 21 CFR Part 11, FDA data integrity guidance, MHRA GxP guidance, and EU GMP Annex 11 (which requires checking that data are not altered in meaning during transfer) turn this into concrete reconciliation and spot-check tasks. - Plan for adoption: A migration succeeds when scientists adopt it at the bench, which depends on named owners, phased milestones, a pilot with real and messy data, and role-based training, as Institut Pasteur did across 12 departments in four waves. SciSure supports the same approach with onboarding, structured data migration from existing ELNs, spreadsheets, and paper records, and key-user and end-user training.
This post was originally written in 2023 and has been updated to reflect more recent regulatory standards governing research, updated customer proof from Institut Pasteur, and SciSure's updated Implementation process.
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If you're replacing an electronic lab notebook, the risky work is preserving the context that makes each record useful later: the sample, protocol version, attachment, instrument output, reviewer, signature, timestamp, storage location, and decision trail.
That context matters for daily lab work and for larger expectations around data integrity, reproducibility, audit readiness, funding, and electronic recordkeeping. For example:
- The NIH Data Management and Sharing Policy expects funded researchers to plan how scientific data will be managed and shared.
- The FAIR Principles push teams toward data that is findable, accessible, interoperable, and reusable.
- FDA-regulated teams need to evaluate electronic records, electronic signatures, access controls, and audit trails under 21 CFR Part 11, FDA's guidance on electronic systems, electronic records, and electronic signatures in clinical investigations, and FDA's data integrity guidance for drug CGMP.
- UK and EU teams should also plan around the MHRA GxP data integrity guidance and EU GMP Annex 11 for computerized systems, which matter when inspectors or quality teams need evidence that records remain accurate, complete, readable, secure, and retrievable.
You don't need to move everything at once. But you do need a structured transition plan that starts with the workflows people already use, proves the new system with real records, and gives scientists a reason to trust the change.
Why do ELN transitions fail?
ELN transitions fail when teams move records before they define the context underlying them, i.e. the workflows, data relationships, ownership, validation checks, and training plan behind those records.
A thin migration plan usually sounds simple: export data from the old ELN, import it into the new one, train users, and go live. In practice, labs get stuck because the old system contains more than notebook entries. It contains partially documented workarounds, folder structures, naming habits, spreadsheet trackers, freezer maps, instrument files, PDF exports, permissions, and local knowledge from the people who kept the system usable.
The better path is to treat your ELN transition as a lab workflow project. You're deciding how your team will document experiments, connect samples to results, preserve supporting files, review completed work, and find evidence later.
What should you audit before leaving your current ELN?
Before leaving your current ELN, audit the records, samples, protocols, files, users, permissions, integrations, paper archives, and compliance requirements that need to survive the move.
Start with a practical inventory. A spreadsheet is fine if that is the fastest way to gather the facts. The point is to surface hidden dependencies before they surprise you halfway through migration.
Capture these categories:
Also ask users where they work outside the current ELN. Many migrations miss the real record because the day-to-day workflow is split across Excel sheets, freezer notebooks, OneNote pages, PDFs, email approvals, local instrument computers, and shared folders.
For multi-site migrations, add site-level detail. A three-site research organization should know which site owns which notebooks, which sample naming rules differ by site, which labs need local admin support, and which historical records must be available during the cutover.
How should you decide what to migrate, archive, or rebuild?
Decide record by record whether content should be migrated as structured data, archived as a preserved record, or rebuilt as a better workflow template in the new ELN. This keeps the transition practical. A full-fidelity migration of every old field can consume months and still recreate messy workflows. A selective migration lets you preserve what matters, retire what is no longer active, and turn common work into better structures.
You can use three buckets.
For active projects, make sure to prioritize the records and data objects that scientists will need in the first 30 to 90 days: current protocols, active sample sets, freezer locations, open studies, recent experiments, high-value attachments, and approval workflows.
For older records, make sure to preserve retrievability. Your team should know where the record lives, what it contains, who owned it, when it was exported, which system it came from, and how to retrieve it during an audit, manuscript review, grant report, IP review, or internal investigation.
What migration services should you look for when moving from paper, spreadsheets, or a legacy ELN?
Look for migration services that cover discovery, paper digitization, data extraction, field mapping, sample cleanup, pilot import, validation, compliance documentation, user training, and controlled archive planning.
If you have 15 or 20 years of paper notebooks and legacy records, the migration service matters as much as the target ELN. You need help turning historical material into a controlled record set without pretending every page can become perfectly structured data.
Useful migration services include:
- Migration discovery.
Inventory systems, paper archives, sample trackers, attachments, signatures, retention needs, and site-level differences before configuration begins. - Paper digitization and indexing.
Scan notebook pages, capture metadata such as notebook owner, project, date range, page range, site, and retention category, and create a retrieval index. - Data extraction and mapping.
Map old ELN fields, spreadsheet columns, sample IDs, aliases, protocol names, and file references into the new system's structure. - Sample and storage cleanup.
Standardize sample types, freezer locations, box positions, ownership, material status, barcode values, and lineage before import. - Historical archive design.
Preserve older records as read-only exports when structured migration carries more cost or risk than day-to-day value. - Pilot migration.
Import a representative dataset first, including awkward records such as duplicate sample names, missing storage locations, large attachments, inactive users, and signed records. - Validation and reconciliation.
Compare source and target counts, attachments, sample links, metadata, timestamps, signatures, permissions, and exception logs. - Compliance package support.
Document migration decisions, test evidence, deviations, approvals, and retention strategy so QA, IT, and auditors can review the process. - Training and adoption support.
Train scientists by workflow, appoint lab champions, and keep a support cadence after go-live.
For an AI-enabled or AI-ready ELN, historical data integrity becomes even more important. AI outputs are only useful if the source records are structured, attributable, access-controlled, and human-verifiable. Before you use any AI layer for search, summarization, protocol drafting, or analysis support, make sure your migration preserves provenance, canonical sample IDs, protocol versions, and file context. The NIST AI Risk Management Framework can help teams think about AI governance, but regulated labs still need the underlying electronic records to stand up to FDA, MHRA, EMA/EU, or internal quality expectations.
How do you protect data integrity during an ELN migration?
You can protect data integrity by proving that migrated records remain complete, accurate, attributable, legible, traceable, and retrievable after the move.
This is where standards become practical. 21 CFR Part 11 calls for secure, computer-generated, time-stamped audit trails for electronic records in scope, plus authority checks so only authorized people can access, alter, or sign records. FDA's guidance on electronic systems, electronic records, and electronic signatures in clinical investigations focuses on whether electronic records and signatures can be trusted as reliable equivalents to paper records and handwritten signatures. FDA's data integrity guidance for drug CGMP emphasizes reliable and accurate data, with risk-based strategies to prevent and detect integrity issues.
For UK GxP environments, the MHRA GxP data integrity guidance is useful because it frames data integrity around the complete lifecycle of the data.
For EU GMP environments, EU GMP Annex 11 is especially relevant during migration because it states that validation should include checks that data are not altered in value or meaning during transfer to another format or system.
For clinical research, ICH E6(R3) Good Clinical Practice is also relevant because it reinforces fit-for-purpose systems, proportionate quality management, and reliable trial records.
If your lab is outside Part 11, GLP, GMP, or GCP scope, these checks still matter. They help you avoid losing scientific context, using the wrong sample, citing an outdated protocol, or spending hours proving that a record is the one you think it is.
How do you build a sample and digital strategy before the move?
Build a sample and digital strategy by defining how samples, metadata, protocols, experiments, files, and system access should connect before you configure the new ELN. Ideally, you should plan these before software configuration. In a real lab, experiments rarely stand alone. They depend on samples, reagents, cell lines, antibodies, plasmids, mouse colonies, freezer boxes, instruments, file outputs, and people who know the history.
This is also where future AI readiness becomes concrete. If your ELN records use consistent sample identifiers, protocol versions, required metadata, and searchable attachments, your team will be in a stronger position to reuse data later. FAIR starts when you decide whether a future scientist can find and understand the record.
How can no-code configuration reduce migration risk?
No-code configuration reduces migration risk when your team can build templates, fields, roles, sample types, and approval workflows around real lab work without waiting for custom software development.
For drug discovery, translational research, and platform biology teams, the question is often practical: can scientists and admins configure complex workflows without heavy custom coding?
A configurable ELN still needs implementation discipline. If every lab group invents its own naming rules and templates, the new system can recreate the old mess in a cleaner interface. Use configuration to standardize the parts of the workflow that need traceability while leaving scientists enough flexibility to document real research.
How should implementation be planned so scientists can adopt the new ELN?
Plan implementation around named owners, phased milestones, real lab workflows, test migration, and role-based training instead of a single go-live date.
Any ELN vendor can show a demo. A successful transition depends on the implementation process behind the software. Your plan should define who owns each decision, what gets configured first, how migration will be tested, who signs off, and how scientists will get help once they begin using the system.
With SciSure, implementation support can include onboarding to assemble a project team, create a project plan, set milestones, appoint key users, and create a training schedule. Our onboarding team can support structured data migration from existing ELNs, spreadsheets, and paper-based records through the SciSure ELN. That matters because scientists are being asked to trust a new way of recording work.
What does implementation for research labs look like in practice?
In research environments, successful implementation looks like phased rollout, local champions, hands-on training, and enough configuration support for scientists to use the system in daily work.
Institut Pasteur, for example, evaluated more than 20 ELNs across 12 research departments, with scientists from around 50 units involved in the final choice. The rollout was organized in four deployment waves, with presentations, follow-up meetings, review meetings, workshops, and monthly training sessions to support onboarding across staff and facilities.
The implementation lesson is specific: a large research organization should expect deployment waves, role-based support, and local users who can translate the system into the way each lab records experiments, samples, protocols, and files.
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How do you train users without slowing active experiments?
Train users with the exact tasks they need for active work: create a record, use a template, link a sample, attach a file, request review, sign or witness, and find the record later. On day one, your scientists need to know how to finish this week's work without losing time or creating compliance questions.
Keep training close to real work. A protein engineering team might practice documenting a construct design, linking plasmid samples, attaching sequencing results, and reviewing the completed record. A bioprocess team might practice linking cell culture samples to scale-up experiments and capturing bioreactor outputs. A core facility might practice sample intake, storage assignment, status updates, and report retrieval.
How should you test the new ELN before full rollout?
Test your new ELN with real workflows, representative migrated data, and the people who will use it every week.
A pilot should be small enough to manage and real enough to expose problems. Include clean sample data and the awkward cases: duplicate sample names, missing freezer positions, old file formats, signed records, external collaborators, inactive users, unusually large attachments, scanned paper notebooks, and experiments with many linked materials.
For regulated teams, document the test cases, expected results, actual results, deviations, fixes, and approval. For research teams outside regulated scope, the same habit gives you a useful project record and reduces uncertainty after launch.
What are some success metrics for an ELN migration?
These metrics help your team see whether adoption is happening in daily work after kickoff.
What can SciSure add to an ELN transition?
SciSure can be a strong option when you need implementation support plus connected ELN, LIMS, sample, inventory, permission, signature, and audit-ready workflows in one platform. You should still evaluate every platform against your lab's workflows, regulatory scope, IT needs, migration risk, and user adoption requirements. The strongest option is the one whose platform and implementation team can help you move from scattered records to usable, trusted workflows.
With SciSure, you can use verified ELN capabilities such as experiment documentation, real-time collaboration, experiment templates, advanced search, instrument integration, approval workflows, version control, variable parameters, mobile access, support and training, and regulatory support for GxP and FDA 21 CFR Part 11 through SciSure ELN. You can also link experiments to samples and inventory, attach files, images, and datasets, and manage configurable roles and permissions.
With SciSure LIMS, you can manage samples, inventory, equipment, storage units, order workflows, barcode labels, custom sample fields, sample history, batch updates, and links between samples and experiments. For transition planning, that matters when your old ELN pain is connected to sample traceability, freezer lookup, inventory status, or manual spreadsheets.
For transition planning, SciSure's practical advantage is the implementation process around the platform. The combination of onboarding, technical implementation, migration support, key-user training, and end-user training can help labs move from "we exported records" to "scientists can use the new workflow."
The lesson for an ELN transition is specific: start with the bottleneck your team already feels. If scientists waste time finding samples, start with sample cleanup and traceability. If protocols vary by person, start with templates and versioned methods. If QA struggles to review completed records, start with signatures, approvals, and audit trails. Implementation succeeds when the first workflow proves value at the bench.
What should happen to the old ELN after go-live?
After go-live, preserve the old ELN as a controlled source of historical records until retention, audit, IP, and retrieval requirements are satisfied. Keep the old system controlled after users begin working in the new one. Decide how long it remains accessible, who can access it, whether users can edit it, and how records will be exported or archived. Your retention plan should account for grant records, publication support, patent evidence, regulated studies, employment changes, institutional policy, and site-level requirements.
If you keep both systems active for too long, users may split their work and create a new traceability problem. If you shut the old system down without a retrieval plan, you may lose context you need later. Aim for a clear transition window and a documented archive.
How do you know your ELN transition is working?
Your ELN transition is working when scientists can complete active work in the new system, find old context when needed, and trust the record during review, reuse, or audit. Look for these signs in daily workflows:
The end state should feel concrete. A scientist can repeat a workflow. A lab manager can locate a sample. A reviewer can trust the record. An admin can control access. A future teammate can understand what happened without tracking down the person who did the work.
FAQ: what should labs know before migrating ELNs?
Use these answers to align lab users, IT, QA, and leadership before you commit to a migration timeline.
Should you migrate every record from your old ELN?
You should prioritize migrating active records and structured data that need to stay searchable, linked, editable, or reportable. Archive older completed records in a controlled, retrievable format when full structured migration does not add value.
How do you migrate 15 or 20 years of paper lab notebooks?
Start by inventorying notebook owners, date ranges, projects, sites, retention needs, and scan priority. Then digitize the highest-value notebooks, index them with searchable metadata, preserve page-level integrity, and connect the archive to active projects or sample records where needed. Do not promise that every historical page will become clean structured data. For many labs, the safer approach is a controlled digital archive plus structured migration for active records, samples, protocols, and high-value datasets.
What migration services help with FDA, MHRA, and EMA expectations?
Look for services that include migration planning, source-data inventory, data mapping, validated test imports, reconciliation logs, exception handling, access-control design, signature and audit-trail review, archive planning, and documented sign-off. FDA, MHRA, and EMA/EU expectations all come back to the same practical question: can your lab prove that records are trustworthy, complete, traceable, secure, and retrievable after the move?
How long does an ELN transition take?
The timeline depends on data volume, data quality, paper archive size, regulatory scope, integrations, validation needs, and how many teams or sites are included. A contained pilot can start faster than an all-lab migration because you only need the first workflow, first template set, first sample dataset, and first user group to prove the approach.
What is the biggest data migration risk?
The biggest risk is losing relationships between records. A notebook entry may still exist after migration, but it loses value if the linked sample, protocol version, attachment, signature, reviewer, instrument output, or storage location is missing.
What should you consider before moving to an AI-powered ELN?
Before you adopt an AI-powered or AI-enabled ELN, make sure your source records are structured, permissioned, traceable, and human-verifiable. AI can only help if the underlying data has reliable sample IDs, protocol versions, metadata, attachments, and provenance. For regulated or audit-sensitive labs, AI outputs should not replace controlled source records, human review, or validated recordkeeping processes.
What no-code ELN options matter for pharmaceutical workflows?
For pharmaceutical workflows, prioritize configurable experiment templates, custom fields, sample types, storage maps, approval workflows, permissions, signatures, barcode workflows, imports, and audit trails. These no-code or low-code controls help admins support complex workflows such as assay execution, sample intake, synthesis, stability testing, formulation, review, and sign-off without creating custom software for every process.
What standards should you consider before changing ELNs?
- NIH's Data Management and Sharing Policy for funded research planning
- FAIR Principles for reuse and metadata quality
- 21 CFR Part 11 and FDA's electronic records and signatures guidance for regulated electronic records
- FDA's data integrity guidance for reliable and accurate CGMP records
- MHRA GxP data integrity guidance for UK GxP data lifecycle expectations
- EU GMP Annex 11 for computerized systems in GMP-regulated work
- ICH E6(R3) for clinical research records,
- and NIST Cybersecurity Framework 2.0 for managing cybersecurity risk around systems, access, and resilience.
When should you consider ELN plus LIMS instead of ELN alone?
Consider ELN plus LIMS when experiment documentation depends heavily on sample tracking, inventory, storage, equipment, order status, barcoding, or batch workflows. If your biggest pain is finding the right sample, proving lineage, managing freezer locations, or connecting materials to results, a connected ELN and LIMS workflow can reduce manual reconciliation.
What should you ask an ELN vendor before signing?
Ask how the vendor handles data migration, paper records, sample links, file attachments, templates, permissions, audit trails, signatures, training, support, validation evidence, integrations, backups, historical archives, and user adoption after go-live. Ask for examples using your workflows and your data shape.
If your current ELN makes it hard to find records, trace samples, standardize protocols, review completed work, or prepare for audits, the cost of staying put is already showing up in small ways. A careful transition gives you a way to fix those problems one workflow at a time.
If you're preparing to move from another ELN, paper notebooks, or legacy research systems, book a SciSure demo to talk through migration scope, implementation support, sample traceability, and adoption planning for your lab.
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